Atrial Fibrillation (AF) is the most common sustained cardiac arrhythmia worldwide, and its complications represent a major public health burden. Patients with AF face a 5-fold increased risk of ischaemic stroke, and AF-related stroke (AFRS) is often more severe than strokes of other origins, leading to higher mortality, poorer functional recovery, long-term disability, and reduced quality of life for patients and caregivers. Despite extensive research and advances in stroke prevention, major challenges remain in understanding the complex links between AF and stroke, tailoring treatment to individual patients, and managing long-term risks such as stroke recurrence or bleeding complications. These challenges are compounded by an ageing population, rising multimorbidity, and increasing pressures on healthcare systems across Europe.
TARGET addresses these unmet needs by developing novel virtual twin-based AI models that combine mechanistic and data-driven approaches with causal AI. These models aim to bridge the gap between research and clinical practice, enabling personalised care at every stage of the AF-related stroke pathway: from prevention and early diagnosis, to acute treatment optimisation and post-stroke rehabilitation. TARGET’s virtual twins will provide dynamic, individualised risk and outcome predictions, offering clinicians and patients new tools to guide decision-making.
TARGET’s objectives:
- Develop personalised risk prediction models for AF and AFRS, grounded in causal understanding of underlying pathophysiological processes and novel biomarkers.
- Optimise treatment and rehabilitation through virtual twin models that can dynamically assess and predict outcomes, tailoring strategies to individual needs.
- Advance causal inference in clinical medicine by elucidating disease drivers and their impact on AF progression and stroke complications.
- Accelerate translational research through multi-scale, multi-organ modelling of the heart, brain, and neuromusculoskeletal system, integrated with real-world clinical and imaging data.
- Develop interoperable decision support tools and a secure data integration and sharing platform to facilitate clinical adoption and sustainability.
- Evaluate facilitators and barriers to implementation of these tools, ensuring that usability, trust, and acceptability among patients and healthcare professionals are embedded from the outset.
TARGET aims to generate impact at multiple levels:
- Scientific impact: advancing virtual twin and causal AI methodologies for personalised medicine, producing new knowledge on the causal mechanisms of AF and AFRS, and identifying novel biomarkers for risk prediction and outcomes.
- Technological impact: delivering secure, and ethically sound decision-support tools and digital health solutions at TRL6, strengthening Europe’s leadership in health technology and digital innovation.
- Societal impact: empowering patients and caregivers with personalised, trustworthy tools; improving quality of life through better prevention, acute management, and rehabilitation; and fostering trust in digital health technologies through co-creation and transparent design.
- Economic impact: reducing the direct and indirect costs of stroke care in Europe and worldwide, through fewer hospitalisations, improved rehabilitation outcomes, and more efficient resource allocation, while building pathways for long-term exploitation and scalability.
